Abstract

The work presented in this thesis is directed towards theinvestigation of the possibilities of using adaptive or learning processes in order to achieve a greater degree of temperature control in a range of furnaces which are normally used for the heat treatment of metals. In this investigation the author has limited his work to two furnaces, a medium sized furnace and a small sized furnace. It is argued that the methods employed in controlling these two furnaces may be readily applied to other furnaces of the same general type.The first objective of the work presented in this thesis was to produce a satisfactory mathematical model for the medium size furnace, check its validity by the use of analogue simulation techniques and finally to use the parameters elucidated from the work on the mathematical model to close the adaptive loop.Next a review of the various methods of system identification was carried out and particular attention was given to the problems associated with the long (several hours) time constants involved in the work on heat treatment furnaces. The difficulties involved when working with long time constants were resolved by making use of a digital controller and by the use of Z-transforins as applied to the furnace mathematical model.The closing of the adaptive loop was achieved by the use of the digital computer as the controller. The identification of the furnace parameters was achieved by a model adjustment strategy and by use of a continually changing index of performance dictated by the monitoring of the apparent changes in the furnaceparameters.Finally the results obtained by controlling the furnace are given, which show that good temperature control has been achieved, but it appears that further work will need to be carried out before a universally acceptable control strategy can be developed.